1.1 Sample Information

A brief sample information is generated from the submission table for the following analysis.

1.2 MERSCOPE Data Quality Summary

The summaries present the data quality assessment automatically generated by MERSCOPE for each experiment. We mainly focus on the transcripts level for each sample. So we’re looking for high density in transcripts, based on the transcripts count per field of view (FOV), transcript density in FOV, and frequency of transcripts detected.

Generally, log10 transcript count > 4.0 in most area can be considered as a good quality standard for human tissue.

Need to note that the low accuracy in DAPI cell boundary is not a concern, as a self-designed cell segmentation processing will take over this task.

1.2.1 NT28530x2(Normal)

1.2.2 TT28519x1(Tumor)

1.2.3 TT28519x2(Tumor)

1.2.4 NT28530x1(Normal)

1.3 Transcript Mis-Match on FOV Boundary

Due to issues with the Vizgen software, transcript alignment is incorrect during decoding, leading to misalignment problems at the field of view (FOV) boundary.

Vizgen has solved this issue by temporarily updating the decoding software to an unpublished beta version.

However, this update also made the “vzg” file being upgraded to “vzg2”. Current Vizgen Visualizer cannot recognize the new data format, which made Visualizer unusable for this dataset.

2.0 Autofluorescent issue

Autofluorescence is a major problem limiting the sensitivity of the detection of the fluorescence specifically derived from the applied dye or probe. We can find it on DAPI imaging.

As Dr. Tan highly suggested, we removed the portion of cell with autofluorescent issue

NT28530_DAPI_Imaging

NT28530_DAPI_Imaging_small

NT28530_dup_DAPI_Imaging

NT28530_dup_DAPI_Imaging_small

2.1 Cell Segmentation & Filtering

Based on the spatial information and images obtained from MERFISH, we developed a machine learning model using the Cellpose algorithm to distinguish individual cells via MERFISH DAPI images.

To ensure the data quality and accuracy of cells, we have defined the minimum and maximum values for cell volume and gene count per cell. The cell volume should be between [100, 2500], and the gene count per cell > 25. After filter the outliers, the qualified cells count is shown in the following table.

Outliers were filtered from the data, and the qualified cell count is presented below. The transcript count Violin and transcript count Spatial Map are displayed here as part of the quality control reveal.

2.1.2 Transcript Count Violin

Transcript Count Violin After Filtering

2.2 Batch Effect & Dimension Reduction

We use Scanpy for the analysis of single-cell level transcriptome data. The initial stage of our analysis involves the elimination of batch effects, thereby ensuring that different samples from various batches are distributed within the same domain and are statistically reasonable to be integrated and compared. To achieve this, we utilize the Harmony algorithm.

Subsequently, we present visualizations of the batch difference by Leiden UMAP clusters. Also, we illustrate the distributions of the Leiden clusters for future analysis.

Umap of cells and colored by batch

3.1 Markers for type annotation

3.2 Cell Type Umap

3.3 Cell Type Spatial Map

3.4 Cancer & T cell & B cell Spatial Map

3.5 Cell Type Count Table

3.6 Cell Type Proportion

4.1 all

4.2 Cancer cell

4.3 Dendritic

4.4 Endothelial

4.5 Fibroblast

4.6 Macrophage

4.7 Mast cell

4.8 Smooth Muscle cell

4.9 T cell